Methods in Ecology and EvolutionBioinformaticsPeer-reviewed journal article
Polymorphism-aware estimation of species trees and evolutionary forces from genomic sequences with RevBayes
Rui BorgesiD, Bastien BoussauiD, Sebastian HöhnaiD, Ricardo J. PereiraiD, Carolin KosioliD
Abstract
O_LIThe availability of population genomic data through new sequencing technologies gives unprecedented opportunities for estimating important evolutionary forces such as genetic drift, selection, and mutation biases across organisms. Yet, analytical methods that can handle polymorphisms jointly with sequence divergence across species are rare and not easily accessible to empiricists. C_LIO_LIWe implemented polymorphism-aware phylogenetic models (PoMos), an alternative approach for species tree estimation, in the Bayesian phylogenetic software RevBayes. PoMos naturally account for incomplete lineage sorting, which is known to cause difficulties for phylogenetic inference in species radiations, and scale well with genome-wide data. Simultaneously, PoMos can estimate mutation and selection biases. C_LIO_LIWe have applied our methods to resolve the complex phylogenetic relationships of a young radiation of Chorthippus grasshoppers, based on coding sequences. In addition to establishing a well-supported species tree, we found a mutation bias favoring AT alleles and selection bias promoting the fixation of GC alleles, the latter consistent with GC-biased gene conversion. The selection bias is two orders of magnitude lower than genetic drift, validating the critical role of nearly neutral evolutionary processes in species radiation. C_LIO_LIPoMos offer a wide range of models to reconstruct phylogenies and can be easily combined with existing models in RevBayes -- e.g., relaxed clock and divergence time estimation -- offering new insights into the evolutionary processes underlying molecular evolution and, ultimately, species diversification. C_LI
- organism: multiple species
- assay: whole-genome sequencing
- research focus: phylogenetics / evolution
- research focus: gene regulatory network inference
- research focus: variant effect prediction
- approach: Bayesian inference
- approach: statistical model
- Citations
- 9
- FWCI
- 0.65
- Field percentile
- top 38%
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